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O&D Forecasting

Issues, Challenges, andForecasting Results

O&D Forecasting

Issues, Challenges, andForecasting Results

John D. Salch

PROS Revenue Management, Inc.

jsalch@prosrm.com

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Forecasting Issues / ChallengesForecasting Issues / Challenges data

processing time

modeling

dynamics

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Data

…There Is More Than We Know What to Do

With

Data

…There Is More Than We Know What to Do

With

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Data CollectionData Collection

Data Sources (Assume 1000 flights per day)

PNR (Touched and Flown) ~ 250,000 per day

Flight level inventory ~ 150,000 per day

Schedule ~ 20,000 per day

Agent, Customer, etc… ~ ?

(your mileage may vary…)

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Data To Collect: Some ExamplesData To Collect: Some Examples

PNR Record Locator Passenger Name Creation Time Creation Date Creation DOW Holiday Special Events Airline Code(s) Origin Airport Origin City Origin Country Origin Continent Destination Airport Destination City Destination Country Destination Continent Path Airport Path City Departure Date(s) all

legs Departure Time(s) all

legs Point of Sale City Point of Sale Country Point of Sale Continent

Booking Office Group Identifier Passenger Type (Freq. Flier

Type?) Frequent Flier Number Fare Classes all legs Number of Passengers Number Protected No Show Identifier No Show Reason Go Show Identifier Go Show Booking Time before

Departure Connection from Airline Connection to Airline Original Point of Departure Final Destination Cancellation Identifier Cancellation Date Cancellation Time Cancellation Reason Flight Numbers all legs Confirmation Codes all legs Fare (Base, Airport Chg, Tax)

Ticketing Information Currency (Type/Exchange Rate) Fare Basis Code Special Service Passenger Address OAL Booked By OAL segment(s) Tour Segment Hotel Segment Car Segment Group Name Number of Passengers in PNR Ticket Type Denied Boardings Code Form Of Payment Info Agent Iata# Tel # Other Supp. Info Messages Protected History (all legs bkd) Received From (PNR modifying

person) Arrival Times all legs OAL segment

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Data ChallengesData Challenges

Rich source of data

It will take many years to find all of the gems

Large volumes of data

Processing time is the binding constraint

Cleaning / Massaging

Lots of cleaning required

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Forecast Modeling

It Must Be Fast, Fast, Fast….

Forecast Modeling

It Must Be Fast, Fast, Fast….

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Forecast UpdatingForecast Updating

Unconstrain Actuals

Update Models

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UnconstrainingUnconstraining

Methods for adjustment

Projection Methods

Iterative Methods

Inputs

Constraint Probability

Bookings / Cancels / Waitlist

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Forecast ModelingForecast Modeling

Bayesian forecasting paradigm Correlation adjustments Seasonality Adjustments Hierarchical Correlation Component Relationship

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Bayesian ForecastingBayesian Forecasting

Simple updating

Minimal data history required

Uses all history, but minimize database

Dynamic to changing data

exponential smoothing

( , )Y Yt t t 1 ( , )Y Yt t t 1

t t tY ( , )1 t t tY ( , )1

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Bayesian ForecastingBayesian Forecasting

components:

reservations (arrivals model)

cancellations (rate model)

go-shows

no-shows

booking curve

Each component poses new challenges!

( , )Y Yt t t 1 ( , )Y Yt t t 1

t t tY ( , )1 t t tY ( , )1

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Correlation AdjustmentCorrelation Adjustment

remove model assumptions of independence across time slices

adjust based on correlation model

early surge in bookings/cancels may result in lower or higher bookings later in cycle

significant reduction in errors

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Seasonality AdjustmentsSeasonality Adjustments

Model cyclical patterns

day of week patterns

monthly patterns

year over year patterns

significant reduction in errors

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Hierarchical AdjustmentsHierarchical Adjustments

remove model assumptions of independence between entities relate entities through hierarchy

reduce “small numbers” problem

high demand in one itinerary may imply high/low demand in another (spill)

significant reduction in errors

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Component RelationshipComponent Relationship

“Blend”:

blend different models to form “out” passenger forecasts, demand to come

relate forecasts, e.g. cancels and no-shows

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Accuracy: “The Forest and the Trees”

Accuracy: “The Forest and the Trees”

Small numbers accurate, but...

aggregations need to be accurate, as well

Feedback mechanism

proper model tuning

bad aggregate forecasts can bias bid prices

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Holidays / Special EventsHolidays / Special Events

Accounted for in models

Discount from “non-holiday” forecasts

Incorporate user knowledge

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Dynamics

Everything Is Always Changing…

Dynamics

Everything Is Always Changing…

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DynamicsDynamics

Schedule changes

Reduce impact of frequent changes in the flight network

Maintain “relevant” history

Create a “schedule-free” network

Accounting for new markets

sponsorship

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Hard Work Pays off...Hard Work Pays off...

Forecasting Results

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Individual ODE MSPEForecasting Dates: 7/6/97 - 4/5/98 (n=40)

0

0.2

0.4

0.6

0.8

1

1.2

0 20 40 60 80 100

Days to Departure

Mea

n Sq

uare

d E

rror

Version 1 Version 2 Version 3 Mean

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Individual ODE MSPE(Forecasting Date: 7/19/98 , n=1)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 20 40 60 80 100

Days to Departure

Mea

n Sq

uare

d E

rror

Version 1 Version 2 Version 3 Mean

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Total Network Demand(Forecasting Date: 7/19/98 , n=1)

0

1000

2000

3000

4000

5000

6000

0 20 40 60 80 100

Days to Departure

Dem

and

Version 1 Version 2 Version 3 Mean Actual

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Leg vs. O&D

0

10

20

30

40

50

60

Day Out

Avg

. M

SP

E

Hierarchical ODAggregated

Simple Linear Model(Leg)

ExponentialSmoothing (Leg)

Hierarchical (Leg)

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Percent Improvement

0

0.05

0.1

0.15

0.2

0.25

3 10 17 24 31 38 45 52 59 66 73 80 87 94 101

Days Out

Per

cen

tag

e Im

p.

ove

r S

imp

le

Leg to O&D Hier

Leg to Leg Hier

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A ForecastA Forecast

"Tonight's forecast: dark. Continuing dark throughout the night and turning to widely scattered light in the morning." - George Carlin